Reference no: EM133115785
BCIS 5110 Programming Languages for Business Analytics - University of North Texas
Description
You will apply your knowledge and skills of Python programming and business analytics to organize and analyze real-life data for actionable insights. Following points enumerate key points to include in your project. These points are not exhaustive. Your group may decide to add content to these points. If your group identifies that some of these points are not applicable for your project, consult the instructor.
Identify dataset
Each group will identify a dataset. I recommend identifying multiple datasets, brainstorm possible questions you may ask, and possible insights. If desired, you could discuss these options with the instructor. Data can come from different sources:
• Directly from companies, organizations or people that you know. Before using such datasets, you will need written permission to share the results with the instructor. In certain cases, the instructor may require a review of the original dataset.
• Datasets available online from organizations, government agencies or universities, etc. Browse websites such as UCI, kdnuggets.com and Kaggle.com (or any other online data source you are aware of) for available datasets.
• Collected by you. You may get data from the web.
Identify research questions
Broadly, your project should include descriptive and predictive questions. Descriptive questions describe the data. Examples include: what is the average number of downloads for an App in Google Play Store? Who is the best salesman in the Northeast region? How does the price change over the years? Is housing price correlated with zip code? You can usually answer them with summary statistics or graphs. Such questions are part of the team's data exploration. You can have plenty of descriptive questions to understand your data. You may present the most interesting ones in your report or presentation.
Predictive questions intend to predict variable outcomes based on data. Examples include: what is the prediction for next month's sale? Is the customer going to default on
their loan? What might be the price for this house? What is the risk of the patient getting readmitted? You will need to build predictive models to answer these questions. Each project should have at least one predictive question.
Analysis
Explore Data
Examine the data. You may want to find about:
• Are there quality issues in the dataset (noisy, missing data, inconsistent, etc.)?
• What will you need to do to clean and/or transform the raw data for analysis?
• Explore each variable and the relationships between variables (Graphs and statistics).
Model Analysis
• Build predictive models: you may want to try a variety of methods.
• Evaluate the model performance.
Deliverables
Project Report
Each team will document their analysis and results in a report. Report will consist of following sections:
o Cover page (title, group members)
o Executive summary
o Project motivation/background
o Key questions
o Data source
o Data description
o Data transformation/Exploratory data analysis
o Models and analysis
o Findings and managerial implications
o Conclusions
o Appendix: python codes with proper documentations
o References, if any
One report per group for submission. Messy or hard-to-read reports will receive penalty. Feel free to add other sections if needed. If you feel any of the above required sections should not be included in the final report, please talk to me. Missing important parts will lead to penalty. There is no page limit.
Presentation
Each team will present their project to the class. Presentation will focus on business problem, analysis, and implications for the business. Logistics related to project presentation will be provided later.
Attachment:- Programming Languages for Business Analytics.rar